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[Wav2Vec2] Fix normalization for non-padded tensors (#13512)

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* finalize

* Apply suggestions from code review

* finish cleaner implementation

* more tests

* small fix

* finish

* up
This commit is contained in:
Patrick von Platen
2021-09-10 15:27:16 +02:00
committed by GitHub
parent c63fcabfe9
commit d7b3b709d0
5 changed files with 144 additions and 59 deletions
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51
tests/test_feature_extraction_speech_to_text.py
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@@ -136,18 +136,49 @@ class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
def test_cepstral_mean_and_variance_normalization(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
inputs = feature_extractor(speech_inputs, padding=True, return_tensors="np", return_attention_mask=True)
input_features = inputs.input_features
attention_mask = inputs.attention_mask
fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
def _check_zero_mean_unit_variance(input_vector):
self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 16, None]
var_tolerances = [1e-3, 1e-3, 1e-1]
for max_length, padding, var_tol in zip(max_lengths, paddings, var_tolerances):
_check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
_check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
_check_zero_mean_unit_variance(input_features[2, : fbank_feat_lengths[2]])
inputs = feature_extractor(
speech_inputs, padding=padding, max_length=max_length, return_attention_mask=True
)
input_features = inputs.input_features
attention_mask = inputs.attention_mask
fbank_feat_lengths = [np.sum(x) for x in attention_mask]
def _check_zero_mean_unit_variance(input_vector, var_tol=1e-3):
self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < var_tol))
_check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]], var_tol)
_check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]], var_tol)
_check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]], var_tol)
def test_cepstral_mean_and_variance_normalization_np(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 16, None]
var_tolerances = [1e-3, 1e-3, 1e-1]
for max_length, padding, var_tol in zip(max_lengths, paddings, var_tolerances):
inputs = feature_extractor(
speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True
)
input_features = inputs.input_features
attention_mask = inputs.attention_mask
fbank_feat_lengths = [np.sum(x) for x in attention_mask]
def _check_zero_mean_unit_variance(input_vector, var_tol=1e-3):
self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < var_tol))
_check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]], var_tol)
_check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]], var_tol)
_check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]], var_tol)
def test_cepstral_mean_and_variance_normalization_trunc(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())